Source language phrases that included names and numbers were not paraphrased.

koehn and knight (2003) show how monolingual texts and parallel corpora can be used to figure out appropriate places to split German compounds.


We were able to measure a translation improvement for all sizes of training corpora, under both the single word and multi-word conditions, except for the largest Spanish-English corpus.

we can extend the definition of the paraphrase probability to include multiple corpora, as follows: where c is a parallel corpus from a set of parallel corpora C. multiple corpora may be used by summing over all paraphrase probabilities calculated from a single corpus (as in Equation 1) and normalized by the number of parallel corpora.


The training corpus comprised 62,000 FBIS segment alignments, and the development "dev" corpus comprised a disjoint set of 2,306 segment alignments from the same FBIS corpus.

the approach yields trainable, probabilistic distortion models that are global: they assign a probability to each possible phrase reordering.


The way to show that one graph element does not follow from another is to make the cost of aligning them high.

the best-known such work has occurred within the field of question answering (Pasca and Harabagiu, 2001;Moldovan et al., 2003); more recently, such work has continued with greater focus in addressing the PASCAL Recognizing textual Entailment (RtE) Challenge (Dagan et al., 2005) and within the U.S. Government AQUAINt program.


The central assumption behind the method is that verb entailment relations manifest themselves in the regular co-occurrence of two verbs inside locally coherent text.

this seems to suggest that several initial and final clauses of adjacent paragraphs are also likely to contain information useful to the model.


On the one hand, the task mimics many possible practical applications of the entailment resource, such as sentence ordering, where, given a sentence, it is necessary to identify among several alternatives another sentence that either entails or is entailed by the given sentence.

in order to score the double-slot templates in the evaluation material, we used the following procedure.


On the other hand, we frequently observe sentences of the second type in corpora, and our method generates the paraphrases from the verb-verb cooccurrences taken from such sentences.

examples of the inference rules acquired by Score are shown in Figure 7 along with the positions in the ranking and the numbers of judges who judged the rule as being proper.


For all of these corpora, we generated realistic substitutes for the [REMOVED] tokens using dictionaries (e.g., a dictionary of names from US Census Bureau) and patterns (e.g., names of people could be of the formats, "Mr. F. Lastname", "Firstname Lastname", "Lastname", "F. M. Lastname", etc.

we built an SVMbased system that, given a target word (TW), would accurately predict whether the TW was part of PHI.


We further propose a domain-aware cross validation strategy to help choose an appropriate parameter for the rank-based prior.

nER is a fundamental task in many natural language processing applications, such as question answering, machine translation, text mining, and information retrieval (Srihari and Li, 1999;Huang and Vogel, 2002).


The smaller the rank r T (f ) is, the more important the feature f is in the training set T .

we use FEX 1 for feature extraction and BBR 2 for logistic regression in our experiments.


In general, when the test data is similar to the training data, IG (or CHI) is advantageous over F (Yang and Pedersen, 1997).

we used a simple string matching method with slight relaxation to tag the gene mentions in the abstracts.


We thank Richard Sproat, ChengXiang Zhai, and Kevin Small for their useful feedback during this work, and the anonymous referees for their helpful comments.

identification of the entity's equivalence class of transliterations is important for obtaining its accurate time sequence.


Both (Cucerzan and Yarowsky, 1999) and (Collins and Singer, 1999) present algorithms to obtain NEs from untagged corpora.

in keeping with our objective to provide as little language knowledge as possible, we introduced a simplistic approach to identifying transliteration equivalence classes, which sometimes produced erroneous groupings (e.g.


In this work, we make two independent observations about Named Entities encountered in such corpora, and use them to develop an algorithm that extracts pairs of NEs across languages.

in order to reduce running time, some limited preprocessing was done on the Russian side.


For time sequence matching, we used a scoring metric novel in this domain.

starting at the 6th iteration, the three are with 3% of one another.


We have demonstrated that using two independent sources of information (transliteration and temporal similarity) together to guide NE extraction gives better performance than using either of them alone (see Figure 3).


We ranked the bad examples by their credit_inst values and their frequency of decreasing the learner's performance in the 10 rounds.

a feature is regarded as good if its credit_feat value is positive.


Using either method of uncertainty sampling, the computational cost of picking an example from T candidates is: O(TD) where D is the number of model parameters.

this sample selection criterion was enforced by calculating a training utility function.


An example can be bad for many reasons: conflicting features (indicative of different senses), misleading features (indicative of non-intended senses), or just containing random features that are incorrectly incorporated into the model.

when the training set was extended, the learner's performance dropped and eventually returned to the same level of the upper bound.


This difference is perhaps partly attributable to the use of oracle part-of-speech tags.

czech, a morphologically complex language in which root identification is far from straightforward, exhibits the worst performance at small sample sizes.


The best-first search method of  estimates Equation 1.

each constituent type found in "S →NP VP ."


We call these probabilities generative probability of a case slot, and they are estimated from case structure analysis results of a large corpus.

they made use of a probabilistic model defined by the product of a probability of having a dependency between two cooccurring words and a distance probability.


The proposed method gives a probability to each possible syntactic structure T and case structure L of the input sentence S, and outputs the syntactic and case structure that have the highest probability.

when b h i is a noun bunsetsu, C i is an embedded clause in b h i .


The similarity is measured using a thesaurus (Ikehara et al., 1997).

the experimental results for syntactic analysis on web sentences show that the proposed model significantly outperforms known syntactic analyzers.


For example, consider the sentence "In relation to Bush's axis of evil remarks, the German Foreign Minister also said, Allies are not satellites, and the French Foreign Minister caustically criticized that the United States' unilateral, simplistic worldview poses a new threat to the world".

after parsing the sentence, it extracts features such as the syntactic path information between each candidate <H> and the expression <E> and a distance between <H> and <E>.


They define the opinion holder identification problem as a sequence tagging task: given a sequence of words ( ) in a sentence, they generate a se- ) indicating whether the word is a holder or not.

they define the task as identifying opinion sources (holders) given a sentence, whereas we define it as identifying opinion sources given an opinion expression in a sentence.


For example, "I think it is an outrage" or "I believe that he is smart" carry both a belief and a judgment.

this is the smallest unit of opinion that can thereafter be used as a clue for sentence-level or text-level opinion detection.


We then read each thread and choose the message that contained the best answer to the initial query as the gold standard.

what makes threaded discussions unique is that users participate asynchronously and in writing.


We again conduct contrastive experiments using both the clean focused read speech and the more challenging broadcast news data.

in the first experiment with the cleanest data, we used only focused syllables from the read Mandarin speech dataset.


Recognition of tone and intonation is essential for speech recognition and language understanding.

since these approaches are integrated with HMM speech recognition models, standard HMM training procedures which rely upon large labeled training sets are used for tone recognition as well.


Carrying complete information with each letter allows the LTS system to be constructed directly and without mistake.

the third (rightmost) column is the ratio of the second divided by the first.


Syntax-based MT offers the potential advantages of enforcing syntaxmotivated constraints in translation and capturing long-distance/non-contiguous dependencies.

we can sidestep this mistake through sisterhood-annotation, which yields the relabeled rules 3 and 4 in Figure 7.


The PTB purposely eliminated such distinctions; here we seek to recover them.

section 3 explores different relabeling approaches and their impact on translation quality.


Section 3 explores different relabeling approaches and their impact on translation quality.

this can lead to tags that are overly generic.


Table 1 shows results for IBM model 4, phrase-based SMT, and LFG-based SMT, where examples that are in coverage of the LFG-based systems are evaluated separately.

the second step is based on features 11-13, which are computed on the strings that were actually generated from the selected n-best f-structures.


It prevents the extraction of a transfer rule that would translate dankbar directly into appreciation since appreciation is aligned also to zutiefst and its f-structure would also have to be included in the transfer.

we compared our system to IBM model 4 as produced by GIZA++ (Och et al., 1999) and a phrasebased SMT model as provided by Pharaoh (2004).


By imagining the left-hand-side trees as special nonterminals, we can virtually create an SCFG with the same generative capacity.

it has been shown by Shapiro and Stephens (1991) and Wu (1997, Sec.


Our work shows how to convert it back to a computationally friendly form without harming much of its expressiveness.

in the first case, we first combine NP with PP: : pq where p and q are the scores of antecedent items.


As shown in Section 3.2, terminals do not play an important role in binarization.

the algorithm will scan through the whole c as if from the empty stack.


• We examine the effect of this binarization method on end-to-end machine translation quality, compared to a more typical baseline method.

we build synchronous trees when parsing the source-language input, as shown in Figure 1.


Second, students write and then may modify their physics essay at least once during each dialogue with ITSPOKE.

once our user affect annotations are complete, we can further investigate their use to predict student learning and user satisfaction.


As shown in Table 1, all subjects in our 3 corpora took the pretest and posttest.

for these 2 corpora, ITSPOKE used an updated speech recognizer further trained on the SYN03 corpus.


User affect parameters further improved the predictive power of one student learning model for both training and testing.

the combined PR05+SYN03 corpora contains subjects drawn from the same subject pool (2005) as the SYN05 test data, and also contains subjects who interacted with the same tutor voice (synthesized) as this test data.


The state with the plusses is the positive final state, and the one at the bottom is the negative final state.

the third columns shows the weighted figures of % Policy Change.


So at the end, 20 random orderings with 20 cuts each provides 400 MDP trials.

counting the number of differences does not completely describe the effect of the feature on the policy.


In all our models, to simplify we assume that the sentence change information is known (as is common with this corpus (Shriberg et al., 2004)).

questions and statements typically have longer, and more complex, discourse structures.


", "merge" is the context word), the context information is helpful.

bunescu and Mooney (2005) propose a shortest path dependency kernel for relation extraction.


Corpus: we use the official ACE corpus for 2003 evaluation from LDC as our test corpus.

although this kernel shows non-trivial performance improvement than that of Culotta and Sorensen (2004), the constraint makes the two dependency kernels share the similar behavior: good precision but much lower recall on the ACE corpus.


The ACE corpus is gathered from various newspaper, newswire and broadcasts.

the effective scope of context is hard to determine.


A system that can accurately discover knowledge that is only implied by the text will dramatically increase the amount of information a user can uncover, effectively providing access to the implications of a corpus.

low precision patterns may have lower weights than high precision patterns, but they will still influence the extractor.


One recent and much more successful approach to part-of-speech learning is contrastive estimation, presented in Smith and Eisner (2005).

unsupervised learning, while minimizing the usage of labeled data, does not necessarily minimize total effort.


For this domain, we utilized a slightly different notion of distributional similarity: we are not interested in the syntactic behavior of a word type, but its topical content.

our general approach is to use distributional similarity to link any given word to similar prototypes.


For example, in "mavi masalı oda" (= the room with a blue table) the adjective "mavi" (= blue) modifies the noun root "masa" (= table) even though the final part of speech of "masalı" is an adjective.

examples of statistical and machine learning approaches that have been used for tagging include transformation based learning (Brill, 1995), memory based learning (Daelemans et al., 1996), and maximum entropy models (Ratnaparkhi, 1996).


An important property for a probabilistic context-free grammar is that it be consistent, that is, the grammar should assign probability of one to the set of all finite strings or parse trees that it generates.

let G = (N, Σ, S, R), and assume that G is not consistent.


Like the algorithm of (Mohri, 1997), this Figure 4: a) Portion of a transducer before determinization; b) The same portion after determinization algorithm will terminate for automata that recognize finite tree languages.

if we choose v then a valid vector pair q w is q , w .


When the top 500 derivations of the translations of our test corpus are summed, only 50.6% of them yield an estimated highest-weighted tree that is the same as the true highest-weighted tree.

for the finite case the previous method produces an automaton with size on the order of the number of derivations, so the technique is limited when applied to real world data.


This is achieved by limiting the number of entry pairs with positive labels for each document: x e i ,e j ≤ m (5) Notice that the number m is not known in advance.

baseline Clustering is a natural baseline model for our partitioning problem.


One possibility relates to the extreme brevity of the summaries: because the summaries are only 350 words in length, it is possible to have two summaries of the same meeting which are equally good but completely non-overlapping in content.

to further gauge speaker activity, we located areas of high speaker interaction and indicated whether or not a given dialogue act immediately preceded this region of activity, with the motivation being that informative utterances are often provocative in eliciting responses and interaction.


We would also like to more closely investigate the relationship between areas of high speaker activity and informative utterances.

they both extract informative dialogue acts, but not the same ones.


The Bayesian model promises settings free of overtraining, and thus more accurate judgements in terms of √ mse and individual nugget classification accuracy.

in fact, we were often tempted to add new nuggets!


As previously discussed, a strict vital/okay split translates into a score of zero for systems that do not return any vital nuggets.

", which cannot be answered by simple named-entities.


We present a methodology for creating a test collection of scientific papers that is based on the Cranfield 2 methodology but uses a current conference as the main vehicle for eliciting relevance judgements from users, i.e., the authors.

or the reference could have been dropped without damaging the informativeness of your paper.


These matter any statistical inference on shallow pools.

rSVM at small qrels of less than 100 documents is the best whilst that is MTF qrels of more than 150 documents.


we thank Jing Jiang and Azadeh Shakery for helping improve the paper writing, and thank the anonymous reviewers for their useful comments.


None of these capabilities are provided by text search engines.

it is obviously an approximative representation.


in what follows, we will assume that there is always at least one path in the lattice that satisfies all of the constraints.


Because of the infrequency, the hard constraints still help most of the time.

formally, a soft constraint C: Y * → R − is a mapping from a label sequence to a non-positive penalty.


To deal with this, we present a constraint relaxation algorithm.

constraint relaxation is more than sixteen times faster than ILP despite running on a slower platform.


Another is a CRF, where M (x) is a lattice with sums of logpotentials for arc weights.

we show that in practice, the method is quite effective at rapid decoding under global hard constraints.


Careful error analysis shows that one important cause for this degradation in performance is the fact that there is insufficient training data for the system to reliably separate support verbs from other verbs and determine whether the constituents outside the NP headed by the nominalized predicate are related to the predicate or not.

the best the system can do is to correctly label all arguments that have a constituent with the same text span in the parse tree.


A semantic parser is learned given a set of sentences annotated with their correct meaning representations.

these methods are mostly based on deterministic parsing (Zelle and Mooney, 1996;Kate et al., 2005), which lack the robustness that characterizes recent advances in statistical NLP.


Human efforts are preferred if the evaluation task is easily conducted and managed, and does not need to be performed repeatedly.

the first two levels employ a paraphrase table.


For years, the summarization community has been actively seeking an automatic evaluation methodology that can be readily applied to various summarization tasks.

it is unknown how large a parallel corpus is sufficient in providing a paraphrase collection good enough to help the evaluation process.


Candidate Selection We assume that words from the reference sentence that already occur in the system generated sentence should not be considered for substitution.

for someone born here but has been sentimentally attached to a foreign country far from home, it is difficult to believe this kind of changes.


The name "Buchanan" is represented in Arabic as "by-wkAnAn" and "Richard" is "rytshArd."

in this simple approach, longer pairs of strings are more likely to be matched than shorter pairs of strings with the same number of different characters.


3 is the general smoothing parameter that takes different forms in various smoothing methods.

vector length is assumed to be a constant factor in analyzing the complexity of the clustering algorithms.


This is expected with such a sparse vector representation for documents.

the generation graph can be viewed as a model where documents cite each other.


Vector length is assumed to be a constant factor in analyzing the complexity of the clustering algorithms.

if we use Equation 1, we end up having unnatural probabilities which are irrepresentably small and cause floating point underflow.


The reason for this is not only the language modeling difficulties, but, of course, the lack of suitable speech and text training data resources.

our work was supported by the Academy of Finland in the projects New information processing principles, Adaptive Informatics and New adaptive and learning methods in speech recognition.


For Turkish a conventional n-gram was built by SRILM similarly as for the morphs.

a common approach to find the subword units is to program the language-dependent grammatical rules into a morphological analyzer and utilize that to then split the text corpus into morphemes as in e.g.


Both methods can also be viewed as choosing the correct model complexity for the training data to avoid over-learning.

the memory requirements of network optimization becomes prohibitive for large lexicon and language models as presented in this paper.


Corresponding growing n-gram language models as in Finnish were trained from the Estonian corpora resulting the n-grams in Table 4.

for a subword lexicon suitable for language modeling applications such as speech recognition, several properties are desirable: 1.


The smooth predictor function learned by NLMs can provide good generalization if the test set contains n-grams whose individual words have been seen in similar context in the training data.

though worse in isolation, the word-based NLMs reduce perplexity considerably when interpolated with Model 1.


Interpolation of Model 1, FLM and FNLM yields a further improvement.

there is also no principled way of handling out-of-vocabulary (OOV) words.


TextTiling wrongfully classifies all of these as starts of new topics.

note that lower values of P k are preferred over higher ones.


Following (Olney and Cai, 2005), we built our LSA space using dialogue contributions as the atomic text unit.

existing topic segmentation approaches can be loosely classified into two types: (1) lexical cohesion models, and (2) content-oriented models.


Table 4 gives the WER for the two systems, on the test set.

we use simple heuristic rules for this purpose; 10 rules for reordering and 15 for merging.


Moreover, recognition accuracy is still around 30% on spontaneous speech tasks, in contrast to speech read from text such as broadcast news.

sparsity of the data available for adaptation makes it difficult to obtain reliable estimates of word n-gram probabilities.


Then the task of relation extraction can be formulated as a form of propagation on a graph, where a vertex's label propagates to neighboring vertices according to their proximity.

especially, with small labeled dataset (percentage of labeled data ≤ 25%), this merit is more distinct.


Temporal information is presently underutilised for document and text processing purposes.

this work uses techniques adapted from Seasonal Auto-Regressive Integrated Moving Average models (SARIMA).


This analysis of the input question affects the subset of documents that will be examined and ultimately plays a key role in determining the answers the system chooses to produce.

we initially formulated paraphrase selection as a three-way classification problem, with an attempt to label each paraphrase as being "worse," the "same," or "better" than the original question.


These steps are necessary to avoid decreasing performance with respect to the original question, as we will show in the next section.

the priors for these classes are roughly 30% for "worse," 65% for "same," and 5% for "better".


We plan to study additional variants that these results suggest may be helpful.

arabic is a morphologically complex language with a large set of morphological features.


Our experimental results presented in the next section confirm this benefit.

• Use a meta classifier whose inputs are the outputs of the contrast classifiers in the committee for a class, and whose output is modeled by training it from a separate, randomly sampled data set.


The remainder of this paper is structured as follows: Section 2 briefly reviews details of the CLEF 2005 CL-SR task; Section 3 describes the system we used to investigate this task; Section 4 reports our experimental results; and Section 5 gives conclusions and details for our ongoing work.

retrieval effectiveness increased substantially for all topic languages.


results for German and Czech are much poorer.


He discusses how Centering can be used to define many different metrics of coherence which might be useful for this task.

the fact that M.NOCB is shown to overtake its Centering-based competitors across several corpora means that it is a simple, yet robust, baseline against which other similar metrics can be tested.


Up to 4000 sentences, the entropy strategy and the combined strategy perform similarly.

we also assume that the selected training samples should give the different aspects of learning features to the classification system.


Results show parse accuracy improves significantly, suggesting disfluency filtering may have a broad role in enabling text-based processing of speech data.

our notion of fillers encompasses filled pauses (e.g.


As discussed earlier ( §1), Charniak and Johnson (2001) have argued that speech repairs do not contribute to meaning and so there is little value in syntactically analyzing repairs or evaluating our ability to do so.

for automated analysis of speech data, this means we may freely explore processing alternatives which delete disfluencies without compromising meaning.


The combination of the two approaches yields better performance than any single model in the two cases.

the impact of the contextual role information is also examined in this study.


Features are automatically extracted from each extract (see Table 2).

additional psychological characteristics were computed by averaging word feature counts from the MRC psycholinguistic database (Coltheart, 1981).


one per-son is described as an extravert because the average population is not.

between 5 and 7 independent observers scored each extract using the big Five Inventory (John and Srivastava, 1999).


In the work we present below, we introduce a new HMM approach to extractive summarization which addresses some of the deficiencies of work done to date.

the classifiers used in these methods implicitly assume that the posterior probability for the inclusion of a sentence in the summary is only dependent on the observations for that sentence, and is not affected by previous decisions.


varying 6 β and optimizing the relevant measure for F β ; the points labeled "baseline" show the precision and recall in token and entity level of the baseline model, learned by VP-HMM.

learning methods such as VP-HMM and CRFs optimize criteria such as margin separation (implicitly maximized by VP-HMMs) or log-likelihood (explicitly maximized by CRFs), which are at best indirectly related to precision and recall.


When the labels associated with n 1 and n 2 are different, we can avoid evaluating ∆(n 1 , n 2 ) since it is 0.

(2) The overfitting problem does not occur although the richer space of PTs does not provide better accuracy than the one based on SST.


2 We merge the partial trees output by a semantic role labeller with the output of the parser on which it was trained, and compute PropBank parsing performance measures on the resulting parse trees.

these augmented tags and the new non-terminals are included in the set f , and will influence bottomup projection of structure directly.


1 Accordingly, it is not possible to draw a straightforward quantitative comparison between our PropBank SSN parser and other PropBank parsers.

given the hidden history representation h of a derivation, a normalized exponential output function is computed by the SSNs to estimate a probability distribution over the possible next derivation moves d i .


The underlying process of message generation is based on layered lexical knowledge bases (LKB) and an ontology.

counting the number of words does not include morphology which in Bliss symbols requires additional choices.


Here, mode and σ denote the mode and standard deviation derived from the GlossEx 's confidence value distribution.

finally, the glossary items are ranked based on their confidence values.


Speakers tend to repeat their linguistic decisions rather than making them from scratch, creating entrainment over time.

sOURCE=Map Task has an interaction effect on the priming decay ln(DIsT), both for PP priming (β = −0.024, t = −2.0, p < 0.05) and for CP priming (β = −0.059, t = −4.0, p < 0.0005).


We believe this is not a side-effect of varying grammar size or a different syntactic entropy in the two types of dialogue, since we examine the decay of repetition probability with increasing distance (interactions with DIST), and not the overall probability of chance repetition (intercepts / main effects except DIST).

map Task contains task-oriented dialogue: interlocutors work together to achieve a task as quickly and efficiently as possible.


In the English and Mandarin systems, the lexical and acoustic feature sets perform similarly, and combine to yield improved results.

the tDt-4 audio corpus includes 312.5 hours of English Broadcast News from 450 shows, 88.5 hours of Arabic news from 109 shows, and 134 hours of Mandarin broadcasts from 205 shows.


A more accurate WSD model will in turn yield yet better WDD results, as demonstrated in this paper.

the standard comparison test for the Sen-seval3 is not as conclusive as with SemCor.


Such corroboration is important as the Senseval3 corpus was not part of the data set used to train the WSD algorithm which provided the basis for subject domain assign-ment.

comparatively little effort has been devoted so far to the disambiguation of word subject domains.


In this paper we present a scheme to select relevant subsets of sentences from a large generic corpus such as text acquired from the web.

maximum likelihood estimation of language models is poor when compared to smoothing based estimation.


In typical situations, using one of those strategies should be a good choice-since BIA requires more classes, it makes sense to prefer IOB2 when in doubt.


Nonetheless, this is an interesting result from a linguistic perspective that begs further investigation.

subject animacy information is extracted and represented as four feature columns in our matrix, corresponding to the four subject NP types.


The soft clustering algorithm, called FANNY, is a type of fuzzy clustering, where each observation is "spread out" over various clusters.

moreover, there is no reason to expect that there would be perfect alignment between the Arabic clusters and the corresponding translated Levin clusters, primarily because of the quality of the translation, but also because there is unlikely to be an isomorphism between English and Arabic lexical semantics, as assumed here as a means of approximating the problem.


In the baseline, verbs are randomly assigned to clusters where a random cluster size is on average the same size as each other and as GOLD.

the output is a membership function P (x i , c), the membership of element x i to cluster c. The memberships are nonnegative and sum to 1 for each fixed observation.


Human cognition studies have found that the in front of/behind axis is easier to perceive than other relations (Bryant et al., 1992).

negative training examples for this experiment were selected from the time-periods that elapsed between the follower achieving perceptual access to the object (coming into the same room with it but not necessarily looking at it), but before the Locating description was spoken.


For example, "ACL" has many different definitions, including "Anterior Cruciate Ligament (an injury)," "Access Control List (a concept in computer security)," and "Association for Computational Linguistics (an academic society)."

to the best of our knowledge, no other studies have approached this problem.


Our proposal depends on co-occurrences on a Web page.

this paper proposed a new method for reading proper names.


Our proposal is similar to previous studies in that both use machine learning.

we offer our discussion in Section 5 and conclusions in Section 6.


Retriever differs from such schemes in filtering out low value content and by making obscure sources visible.

unlike a search engine, rather than returning ranked documents links in response to a query, Lycos Retriever categorizes and disambiguates topics, collects documents on the Web relevant to the disambiguated sense of that topic, extracts paragraphs and images from these documents and arranges these into a coherent summary report or background briefing on the topic at something like the level of the first draft of a Wikipedia 2 article.


an undirected link between s i and s j (i j) with affinity weight aff(s i ,s j ) is constructed if aff(s i ,s j )>0; otherwise no link is constructed.

graph-based methods have been proposed to rank sentences or passages.


By trying different bins combinations and different α such that 0 < α < 1 with interval 0.1, we obtained the average optimal α = 0.15 and 0.9 from the Remedia and ChungHwa training sets respectively 7 .

we refined our reference performance level by combining the ME models (MEM) and handcrafted models (HCM).


The clustering model is advantageous over other models in that the flexibility of clustering methods allows "many-to-many" mappings.

the average number of images per document is 6.5±1.5 and the average number of sentences per abstract is 7.2±1.9.


It is composed of three parts: a dictionary-based N-gram word segmentation for segmenting IV words, a subwordbased tagging by the CRF for recognizing OOVs, and a confidence-dependent word segmentation used for merging the results of both the dictionary-based and the IOB tagging.

section 3 presents our experimental results.


Our technique, applied as preprocessing to the source corpus, splits and normalizes surface words based on the target sentence context.

we trained translation parameters for 10 scores (language model, word and phrase count, and 6 translation model scores from (Vogel, 2005) ) with Minimum Error Rate training on the development set.


We finally couple the syntactic-prosodic and acousticprosodic components to achieve significantly improved pitch accent and boundary tone classification accuracies of 86.0% and 93.1% respectively.

the pitch accent and boundary tone detection accuracy at the syllable level were 75% and 88% respectively.


This suggest that accent ratio provides rich information about words beyond that of POS class and general informativeness.

we might expect such a use of raw frequencies to be problematic.


In this section, we show that conversants try to avoid initiative conflicts by examining both the offset of initiative conflicts and the urgency levels.

four cases are because the second conversant has something urgent to say.


Once we understand the conventions people adopt in negotiating initiative, we can implement them in a computer dialogue system to create natural interactivity.

if we predict that the system always wins the conflicts, we achieve 70% accuracy.


The amount of degradation in the overall accuracy (F1) of each of the models in relation to that of the ALL model indicates the contribution of the feature type that has been left out of the model.

in this study we include the following features: the label of the current topic segment, the position of the DA in a topic segment (measured in words, in seconds, and in %), the distance to the previous topic shift (both at the toplevel and sub-topic level)(measured in seconds), the duration of the current topic segment (both at the top-level and sub-topic level)(measured in seconds).


Otherwise, the answer is determined to be no.

the 769 sentences were translated by using five commercial Mt systems to investigate the relationship between subjective evaluation based on yes/no questions and conventional subjective evaluation based on fluency and adequacy.


This is because conventional methods are based on the similarity between a translated sentence and its reference translation, and they give the translated sentence a high score when the two sentences are globally similar to each other in terms of lexical overlap.

the sub-goals of a given sentence should be generated by considering the complexity of the sentence and the alignment information between the original source-language sentence and its translation.


Maximum Correlation Training (MCT) is an instance of the general approach of directly optimizing the objective function by which a model will ultimately be evaluated.

we also give the upper bound for each evaluation aspect by training MCT on the testing MT outputs, e.g., we train MCT on E09 and then use it to evaluate E09.


If we reveal the alignment of the source sentence with both the reference and the MT output, the Chinese word bu neng would be aligned to must not in the reference and must hardly in the MT output respectively, leaving the word not in the MT output not aligned to any word in the source sentence.

evaluation has long been a stumbling block in the development of machine translation systems, due to the simple fact that there are many correct translations for a given sentence.


The most interesting question in this paper is, with all these metrics, how well we can do in the MT evaluation.

there is no guarantee that, starting from a random w, we will get the globally optimal w using optimization techniques such as gradient ascent.


These results show that the strategy of only including the new information as features in a standard n-best re-ranking scenario does not lead to an improvement over the baseline.

we also show that integrating our case prediction model improves the quality of translation according to BLEU (Papineni et al., 2002) and human evaluation.


More recently, (Chiang, 2005) extended phrase-pairs (or blocks) to hierarchical phrase-pairs where a grammar with a single non-terminal allows the embedding of phrases-pairs, to allow for arbitrary embedding and capture global reordering though this approach still has the high overlap problem.

prefixes and suffixes which are specific in translation are limited to their English translations.


Also, for a fixed value of β, BASIC-P1 β gives better MUC-F1 than BASIC-Pa β , and BASIC-Pa β gives better pairwise-F1 than BASIC-P1 β for both data sets.

in experiments on two coreference data sets, structured local training reduces the error rate significantly (3.5%) for one coreference data set and minimally (≤ 1%) for the other.


Our work differs in that (1) we use hidden variables to capture the interactions between local inference and global inference, we present an application to coreference resolution, while previous work has shown applications for variants of sequence tagging.

we must resort to an 5 Single-link clustering simply takes the transitive closure, and does not consider the distance metric.


The proposed twin-model outperforms the baseline without tuning the free parameter.

to implement the twin model, we adopt the log linear or maximum entropy (MaxEnt) model (Berger et al., 1996) for its flexibility of combining diverse sources of information.


We see that both the first-order features and the training enhancements improve performance consistently.

4 Error-driven and Rank-based training of the First-Order Model In this section we propose two enhancements to the training procedure for the First-Order Uniform model.


This process is repeated for a fixed number of iterations.

(we should note that there are also small differences in the feature sets used for error-driven and standard training results.)


Let s + (Λ, x j ) be the unnormalized score for the positive example and s − (Λ, x k ) be the unnormalized score of the negative example.

they do not investigate rank-based loss functions.


Our work is continuing by exploring methods for handling fields with incorrect or corrupted values.

the cross-field inference makes it possible to find documents in response to a structured query when those query fields do not exist in the relevant documents at all.


The model is based on the idea that missing or corrupted values for one field can be inferred from values in other fields of the record.

the model will fail whenever the order of words, or their proximity within a field carries a semantic meaning.


Figure 3(a) shows that country coverage grows much more rapidly for GRASSHOPPER than for MOVIECOUNT.

it is known that the fundamental matrix gives the expected number of visits in the absorbing random walk (Doyle and Snell, 1984).


GRASSHOPPER is an alternative to MMR and variants, with a principled mathematical model and strong empirical performance.

groups of nodes far away from g 1 still allow the random walk to linger among them, and thus have more visits.


The deviation in single letter words can be attributed to the writing system being a transcription of phonemes and few phonemes being expressed with only one letter.

this work proposes a plausible model for the emergence of large-scale characteristics of language without assuming a grammar or semantics.


Neither the Mandelbrot nor the Simon generation model take the sequence of words into account.

to this, the Mandelbrot model shows to have a step-wise rank-frequency distribution and a distorted lexical spectrum.


On the one hand, the improvement suggests that our original feature configuration included some irrelevant features, and in turn confirmed that overinclusion of features could hurt the performance.

(2003) and Culotta and Sorensen (2004) used tree kernels for relation extraction.


The result below follows from Bayes' Rule and our assumptions above: Proposition 1 If two strings s i and s j have P i and P j potential properties (or instances), and they appear in extracted assertions D i and D j such that |D i | = n i and |D j | = n j , and they share k extracted properties (or instances), the probability that s i and s j co-refer is: Substituting equation 1 into equation 2 gives us a complete expression for the probability we are looking for.

web Information Extraction (wIE) systems extract assertions that describe a relation and its arguments from web text (e.g., (is capital of, D.C., United States)).


Many objects appear with multiple names that are substrings, acronyms, abbreviations, or other simple variations of one another.

our probabilistic String Similarity Model (SSM) assumes a similarity function The model sets the probability of s 1 co-referring with s 2 to a smoothed version of the similarity: The particular choice of α and β make little difference to our results, so long as they are chosen such that the resulting probability can never be one or zero.


This algorithm is O(M log M ) where M is the number of distinct strings.

experiments with the extensions, using the same datasets and metrics as above, demonstrate that the Function Filter (FF) and the Coordination-Phrase Filter (CPF) boost ReSOLVeR's performance.


Judging recall would require inspecting not just the clusters that the system outputs, but the entire data set, to find all of the true clusters.

we set P i = N × n i , and we set N = 50 in our experiments.


Ravikumar and Cohen (Ravikumar and Cohen, 2004) present an unsupervised approach to ob-ject resolution using Expectation-Maximization on a hierarchical graphical model.

if R i,j is true then S i,j = min(P i , P j ), and if R i,j is false then S i,j < min(P i , P j ).


For example we can restrict the search space (i.e.

the probability for two words to be related tends to 0 when their similarity is negative (i.e., they are not domain related), supporting the basic hypothesis of this work.


Because we are marginalizing over θ, the trees t i become dependent upon one another.

in our experiments with the Sesotho data above we found that for the small values of α necessary to obtain a sparse solution,the expected rule count E[f r ] for many rules r was less than 1 − α.


The probability of an SCFG rule instance computed by Algorithm 1 can be written in this functional form: where and the MRF has one factor f i for each child nonterminal X i in the grammar rule R. The factor's value is the probability of the child nonterminal, which can be expressed as a function of its four boundaries: For reasons that are explained in the following section, we augment our Markov Random Fields with a dummy factor for the completed parent nonterminal's chart item.

we have shown in the exponent in the complexity of polynomial-time parsing algorithms for synchronous context-free grammars grows linearly with the length of the grammar rules.


For any grammar with maximum rule size n, a fairly straightforward dynamic programming strategy yields an O(N n+4 ) algorithm for parsing sentences of length N .

the overall complexity is unchanged, because each assignment to all variables in each cluster is still considered only once.


For each word, w, in the vocabulary, we check whether (1) w can be segmented as r+x or p+r, where p and x are valid prefixes and suffixes respectively and r is another word in the vocabulary, and (2) the WRFR for w and r is less than our predefined thresholds (10 for suffixes and 2 for prefixes).

our algorithm outperforms the winners for all the languages in the competition, demonstrating its robustness across languages.


There has also been a rethinking of the traditional modular NLG architecture (Reiter, 1994).

this interest does not appear to have translated into practice: of the 30 implemented systems and modules with development starting in or after 2000 that are listed on a key NLG website 1 , only five have any statistical component at all (another six involve techniques that are in some way corpus-based).


Like WASP −1 , the phrase extraction algorithm of PHARAOH is based on the output of a word alignment model such as GIZA++ (Koehn et al., 2003), which performs poorly when applied directly to MRLs (Section 3.2).

finally, we show that hybridizing these two approaches results in still more accurate generation systems.


The three next sentences display some advantages of our approach over the K&M model: here, the latter model performs deletion with too little lexicosyntactic information, and accidentally removes certain modifiers that are sometimes, but not always, good candidates for deletions (e.g., ADJP in Sentence 2, PP in sentences 3 and 4).

first, it appears that vertical annotation is moderately helpful.


While contextual information is the primary source of information used in WSD research and has been used for acquiring semantic lexicons and classifying unknown words in other languages (e.g., Roark and Charniak 1998;Ci-aramita 2003;Curran 2005), it has been used in only one previous study on semantic classification of Chinese unknown words (Chen and Lin, 2000).

similarity between their modifiers, using the concept of information load (IC) of the least common ancestor (LCA) of the modifiers' semantic categories.


To alleviate this problem, the remove-one method is used for testing the knowledge-based models.

8 The rules for four-character words are given in (9).


Among the various knowledge-based (Lesk, 1986;Galley and McKeown, 2003;Navigli and Velardi, 2005) and data-driven (Yarowsky, 1995;Ng and Lee, 1996;Pedersen, 2001) word sense disambiguation methods that have been proposed to date, supervised systems have been constantly observed as leading to the highest performance.

through word sense disambiguation experiments, we show that the Wikipedia-based sense annotations are reliable and can be used to construct accurate sense classifiers.


For each word, the table also shows the number of senses, the total number of examples, and two baselines: a simple informed baseline that selects the most frequent sense by default, 5 and a more refined baseline that 5 Note that this baseline assumes the availability of a sense tagged corpus in order to determine the most frequent sense of a word.

rather than using the senses listed in a disambiguation page as the sense inventory for a given ambiguous word, we chose instead to collect all the annotations available for that word in the Wikipedia pages, and then map these labels to a widely used sense inventory, namely WordNet.


As the early TRECs have found (Voorhees and Tice, 1999), locating a passage that contains an answer is considerably easier than pinpointing the exact answer.

the goal is to quantify the number of relevant facts that a user will have encountered after reading a particular amount of text.


It can be observed that there are seven different ways of re-writing the original query to attain better performance.

the motivation for encouraging this type of querying is that longer queries would provide more information in the form of context (Kraft et al., 2006), and this additional information could be leveraged to provide a better search experience.


It is well-known that an average is easily skewed by outliers.

consider the following query: Define Argentine and British international relations.


The sentencelevel combination was based on re-ranking a merged ¤ -best list using generalized linear models with features derived from each system's output.

this requires aligning the system outputs to form a consensus network and -during decoding -simply finding the highest scoring path through this network.


These weights can balance the total confidence between the number of systems generating the hypothesis (votes), and the sum, maximum and average of the system confidences.

it was found that this approach did not always yield better combination output compared to the best single system on all evaluation metrics.


To decide which of the two paths to merge date into, the algorithm looks at the number of items assigned to the deepest node that is held in common between the existing Core Tree and each candidate path for the ambiguous term.

while manually created metadata is considered of high quality, it is costly in terms of time and effort to produce, which makes it difficult to scale and keep up with the vast amounts of new content being produced.


In order to pick the value of the parameter α for each of the sIB and IDT test experiments, we use "strapping" , which, as we mentioned earlier, is a technique for training a meta-classifier that chooses among possible clusterings.

a task instance refers to a particular example from the class.


Roughly, we expect that if θ h f h (x i , y) is much higher for y = y i than for other values of y, then the annotator's r i is correspondingly more likely to indicate in some way that feature f h strongly influenced annotation y i .

at any given moment, such a tool should allow the annotator to highlight, view, and edit only the several rationales for the "current" annotated entity (the one most recently annotated or re-selected).


If we were using more than unigram features, then simply deleting a rationale substring would not always be the best way to create a contrast document, as the resulting ungrammatical sentences might cause deep feature extraction to behave strangely (e.g., parse errors during preprocessing).

we require w which makes w 0 play the role of a bias term).


Policy Exploration: RL searches the space of polices by determining Q for each state-action pair sa, which is the minimal cost to get to the final state from state s starting with action a.

it is assumed that the user will not change his or her mind depending on what flights are found.


RL gives a way to learn the best action to perform in any given state.

section 5 demonstrates that Is can be used for simulating a dialogue between the system and a user.


This domain is simple enough that we do not need separate understanding and action rules, and so we encompass all reasoning in the action rules, shown in Fig.

rL needs to find a single action that will work for the entire rL state, and so that action should not be considered.


For example, a model can have an optimal policy with a very high ECR value, but have very wide confidence bounds reaching even into negative rewards.

the V-values indicate how much reward one would expect from starting in that state to get to a final state.


All three metrics show that the best feature to add to the Baseline 2 model is Concept Repetition since it results in the most change over the Baseline 2 policy, and also the expected reward is the highest as well.

the approach discussed above assumes that given the size of the data set, the ECR and policies are reliable.


These improvements have also been confirmed by t-tests as significant.

for two users (i.e., user 3 and user 4), the gaze-based salience driven language models consistently outperformed the bigram and trigram models in both early application and late application.


These features have been demonstrated in several successful applications (Duchowski, 2002).

we found out that the learned priming weight performed worse than the empirical one in our experiments.


We can find a similarity also to the PageRank algorithm (Brin and Page, 1998), which has been applied also to natural language processing tasks (Mihalcea, 2004;Mihalcea, 2005).

our attempt is to imitate annotator's decision.


Using H(c), the probability distribution of the network is represented as P (c) = exp{−H(c)}/Z, where Z is a normalization factor.

by estimating a class of a proper noun and finding the words that matches the class in the dictionary, we can predict the semantic orientations of the proper noun based on the orientations of the found words.


The latent variable method is applicable only to instance pairs consisting of an adjective and a seen noun.

an appropriate mapping from the words found in corpus to entries of a dictionary will solve this problem.


The training is performed by iteratively ranking each training input x and updating the model.

if y = y, then the weights and boundaries are updated to improve the prediction for x (step 4.c in Figure 1).


The most important features for deciding attribution to the current paper were the distance features 2(a,c,e), the rank 3(a) and the Hobbs' prediction 1(d).

the three citations above are described as flawed (detectable by "does not provide a very satisfactory account"), and thus receive the label Weak.


To demonstrate the use of automatic scientific attribution classification, we studied its utility for one well known discourse annotation task: Argumentative Zoning (Teufel and Moens, 2002).

the distance between two sets is larger, the smaller their intersection and the larger their union.


The STRIVE metaclassification approach  extended Wolpert's stacking framework (Wolpert, 1992) to use reliability indicators.

while this framework is known to work well for standard classification, its suitability for fusing rankers has not been studied.


If we examine the curves using error bars, we see that the variance of STRIVE drops faster than the other classifiers as we move further along the x-axis.

this work was supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No.


While STRIVE has been shown to provide robust combination for topic classification, a formal motivation is lacking for the type of reliability indicators that are the most useful in classifier combination.

we examine F1 to ensure that an improvement in ranking will not come at the cost of a statistically significant decrease in F1.


: Boeing, Heinz, Staples, Textron Test : General Electric, General Motors, Gannett, The Home Depot, IBM, Kroger, Sears, UPS Ground truth was created from the entire web, but since the corpus for each company is only a small web snapshot, the experimental results are not similar to extraction tasks like MUC and ACE in that the corpus is not guaranteed to contain the information necessary to build the entire database.

the database constraints presented in this paper provide a more general framework for jointly conditioning multiple relationships.


Using constraints on probabilistic databases yields F-Measure improvements of 5 to 18 points on a per-relationship basis over a state-of-the-art multi-document extraction/fusion baseline.

6 Λ η,α (r, t) gives a much higher penalty to relationships which create inconsistencies than does Λ η (r, t).


We describe our pattern extraction algorithm in three steps.

for example, from the previous snippet we extract the pattern, X is the largest X.


Our implementation of Co-occurrence Double Checking (CODC) measure (Chen et al., 2006) reports the second best correlation of 0.6936.

a snippet is a brief window of text extracted by a search engine around the query term in a document.


A large number of frequent content words is often associated with only one dominant sense.

its usefulness is restricted by the availability of sense-annotated data.


Moreover we retain only the sentences in which at least one of the context words is in our previously acquired knowledge-base of near-synonym collocations.


Table 3 shows that the performance is generally better for word counts than for document counts.

: , where N can be ignored in comparisons.


A comparison of the two SMT outputs indicates that integrating the proposed transliteration model into our machine translation system can significantly improve translation utility.

the transliteration models can only execute forward sequential jumps.


Furthermore, by incorporating an automatic spell-checker based on statistics collected from web search engines, transliteration accuracy is further improved.

position information should be considered within the alignment models.


Conversely, a method like pronunciation by analogy (PbA) (Marchand and Damper, 2000) is considered a global prediction method: predicted phoneme sequences are considered as a whole.

unfortunately, proper nouns and unseen words prevent a table look-up approach.


The base dependency parser is deterministic and performs a single scan over the sentence.

the revision stage corrects the output of the base parser by means of revision rules learned from the mistakes of the base parser itself.


Performing several parses in order to generate multiple trees would often just repeat the same steps.

the model only makes a binary decision, which is suitable for the simpler problem of POS tagging.


(2006) that a hierarchically split PCFG could exceed the accuracy of lexicalized PCFGs (Collins, 1999;Charniak and Johnson, 2005).

of course, the naive version of this process is intractable: we have to loop over all (pairs of) possible parses.


For a final grammar G = G n , we compute estimates for the n projections G n−1 , .

treebank estimation has several limitations.


9 Broadly, the CFG projection encodes constituency structure, while the dependency projection encodes lexical selection, and both projections are asymptotically more efficient than the original problem.

sometimes, we might be willing to trade search optimality for efficiency.


The non-factored model, using the approximate projection heuristic, achieves an F 1 of 83.8 on the test set, which is slightly better than the factored model.

the completion cost of a state bounds the sum of the completion costs in each projection.


the text spans to which they apply; these are used as test (W 1 , W 2 ) span pairs for classification.

we find that in Section 24, 13 out of 74 sentences contain a parsing error in the relevant aspects, but the effects are typically small and result from well-known parser issues, mainly attachment errors.


The intuition here is that all sentence boundaries should not be treated equally during RSR instance mining.

noRel is proposed by M&E as a default model of same-topic text across which no specific RSR holds; instances are extracted by taking text span pairs which are simply sentences from the same document separated by at least three intervening sentences.


While there are a number of different relation taxonomies (Hobbs, 1979;McKeown, 1985;Mann and Thompson, 1988;Martin, 1992;Knott and Sanders, 1998), many researchers have found that, despite small differences, these theories have wide agreement in terms of the core phenomena for which they account (Hovy and Maier, 1993;Moser and Moore, 1996).

we would like to make our patterns recognize that some sentence boundaries indicate merely an orthographic break without a switch in topic, while others can separate quite distinct topics.


uses the same grid representation, but treats the transition probabilities P (r i,j | r i,j ) for each document as features for input to an SVM classifier.

the wider variety of grammatical constructions used may motivate more complex syntactic features, for instance as proposed by (Siddharthan et al., 2004) in sentence clustering.


The probability that the optimal path has no repeated colors is: By the amplification analysis, the number of trials needed to drive the failure probability to the desired level will be inversely proportional to this quantity.

we implemented the rest of the decoders in Python with the Psyco speed-up module.


To answer, let us consider the ratio: If this ratio is less than 1, then using r + 1 colors will be faster than using r; otherwise it will be slower.

the requirement that the path be long makes it more similar to the the Traveling Salesman Problem (TSP).


Solving numerically to find where this is equal to 1, we find α ≈ .76804, which yields a running time proportional to approximately (4.5) k .

the running time for the necessary number of trials T r will be proportional to What r ≥ k should we choose to minimize this quantity?


For instance, we plan to extract transfer rules from the aligned source and English structures and also calculate head/modifier crossings between languages similar to those described in (Fox, 2002).

knowing a little about the structure of a language can help in developing annotated corpora and tools, since a little knowledge can go a long way in inducing accurate structure and annotations (Haghighi and Klein, 2006).


The goal of using a second feature set was to examine how dependent prediction quality was on a specific set of features, as well as to test the extent to which the output of syntactic parsing might improve prediction accuracy.

these may be poor assumptions in a real application, but they can be easily included or excluded in the model as desired.


It is widely recognized that one of the best ways to learn a foreign language is through spoken dialogue with native speakers (Ehsani and Knodt, 1998).

this extra step aims at reducing discrepancies caused by syntactic structure differences between the two languages.


Indeed, both are trying to assess the quality of the translation output, whether it is produced by a computer or by a foreign language student.

one has to take care not to bias towards the correct response so strongly that the student is allowed to make mistakes with impunity.


a sample target word and its reference definition, along with examples of humanjudged responses, are given in Sections 3.3 and 4.1.


7 We lowercased the training, development and test sentences.


To overcome this, we use a binary format which is a memory map of the internal representation used during decoding.


In the latter work, the author shows that CP clearly outperforms both the naive single pass solution of severe pruning as well as the naive two-pass rescoring approach.

the evaluation test set contains 500 sentences with an average length of 10.3 source words.


Note that this closely corresponds to the post-editing operation performed on the Job Bank application.

hRSDC kindly provided us with a sample of data from the Job Bank.


With less than 500k words of training material, the phrase-based MT system already outperforms the rule-based MT baseline.

one way around this problem would be to modify the APE system so that it not only uses the baseline MT output, but also the source-language input.


Given the encouraging results of the Portage APE approach in the above experiments, we were curious to see whether a Portage+Portage combination might be as successful: after all, if Portage was good at correcting some other system's output, could it not manage to correct the output of another Portage translator?

when the translation system has been trained using distinct data (Portage Hansard + Portage APE experiments), post-editing makes a large difference, comparable to that observed with the rule-based MT output provided with the Job Bank data.


Video collections are difficult to be general-purpose since hundreds hours of videos might take tens of hundreds GB storage space.

we therefore propose two algorithms to 1): re-tokenize an input subsequence, and 2): compute the DP score for a subsequence.


For example, the verb "make" uses Arg2 for the "Material" argument; but the verb "multiply" uses Arg2 for the "Extent" argument.

propBank has been widely used as training data for Semantic Role Labeling.


On the Wall Street Journal (WSJ) data, using correct syntactic parses, it is possible to achieve accuracies rivaling human interannotator agreement.

as usual, argument classification is measured as percent accuracy (a), whereas ID and ID + Class.


Table 7 shows a comparison of these conditions.

there is a considerable drop in Classification accuracy (86.1% vs 93.0%).


Table 1 summarizes the quality of the systems selected according to the Accuracy criterion.

we applied CBC to the TREC-9 and TREC-2002 (Aquaint) newswire collections consisting of over 600 million words.


We can see that the two approaches agree on the top four pairs, but disagree on the rest in the list.

among 75 skill pairs, 60 of them were rated correctly (i.e., 80% accuracy), which significantly outperforms the statistical approach, and is very close to the upper bound accuracy, i.e., human agreement (81%), as shown in Figure 2.


For our task, the correctness of the prepositional phrase attachment is especially important for extracting accurate semantic role patterns (Gildea and Jurafsky, 2002 .

[ action Apply] [ theme Knowledge of [ concept IBM Ebusiness Middleware]] to [ purpose PLM Solutions] In this example, "Apply" is the "action" of the skill; "Knowledge of IBM E-business Middleware" is the "theme" of the skill, where the "concept" semantic role (IBM E-business Middleware) specifies the key component of the skill requirement and is the most important role for skill matching; "PLM Solutions" is the "purpose" of the skill.


The term signature wants to capture the notion that the data it embodies is truly representative of a particular item, and that shows the details of its typical behavior.

all vectors produced, one per occurrence of the word in question, are stored then in a kind of vector of vectors that we have called its signature.


Using the master method (Cormen et al, 2001), P(N) = O(log 2 N) in the best case (α=1).

where α = 2−p, when p is the probability that the popular half contains sufficient matches.


These devices have been researched and are starting to become commercially available (e.g.

a very simple way to prune phrase pairs from a translation model is to use a probability threshold and remove all pairs for which the translation probability is below the threshold.


With the exception of Fraser and Marcu (2006), these previous publications do not entirely discard the generative models in that they integrate IBM model predictions as features.

to generative models, this framework is easier to extend with new features.


While other work has shown similar performance on this type of dataset, our approach presented here is faster and does not require training.

parallel corpora have many uses in natural language processing, and their dearth has been identified as a major bottleneck (Diab, 2004).


We attribute the difference in speed and BLEU score between our system and Pharaoh to the fact Value Elimination searches in a depth-first fashion over the space of partial configurations of RVs, while Pharaoh expands partial translation hypotheses in a best-first search manner.

to each English word e i corresponds a conditionally dependent fertility φ i , which indicates how many times e i is used by words in the French string.


This definition of an event follows from the fact that most events in baseball must start with a pitch and usually do not last longer than four shots (Gong et al., 2004).

whereas visual context features provide information about the global situation that is being observed, camera motion features afford more precise information about the actions occurring in the video.


Because data is sparse, the situation model is trained only on the hand annotated highlight events.

α=0), and both information together (i.e.


Incorrect answers occur significantly more than expected, and the dependencies are stronger for uncI.

non-uncertain answers occur significantly less (-), or aren't significantly dependent (=).


These filter responses, which can be computed very efficiently, are used as input to a learning algorithm that generates the final detector.

the experiments performed yield interesting results.


We know from results of CVA and BLEU analyses that for both groups of speakers, higherscoring essays are more lexically similar to the prompts.

since better essays are presumably better at expressing the content of the prompts, we can hypothesize that native speakers paraphrase the content more than non-native speakers.


This paper investigates the properties of large nbest lists in the context of statistical machine translation (SMT).

from the baseline of 31.5%, we only get a moderate improvement of approximately 0.5% BLEU.


The highest n that fit into the 16GB machine was 60 000.


We present a method that allows for fast extraction of very large n-best lists based on the k shortest paths algorithm by (Eppstein, 1998).

most reference translations would never have been selected as final candidates.


More recently, new optimization methods have been used to scale-up transductive SVMs to large data sets (Collobert et al., 2006), however we did not face scaling problems in our current experiments.

the experiments were run on the Mastodon cluster provided by NSF grant EIA-0303609.


However, it requires considerable human effort to annotate sentences.

in the first iteration, the set of positive examples for production contains all sentences whose corresponding MRs use the production in their parse trees.


We could certainly improve the association score model, for example adding discount factors or adding more association score types, or dictionaries.

taking theses issues into account, we implemented the following features: • distinct source and target unlinked word penalties: since unlinked words have a different impact whether they appear in the source or target language, we introduced an unlinked word feature for each side of the sentence pair.


In this paper we explored the use of rich syntactic features for the relation extraction task.

comparison between the PAK model and SRL model shows that manually specified features are more discriminative for binary relation extraction; they boost precision and accuracy for ternary relation extraction.


Table 3: Percent scores of Precision/Recall/F-score/Accuracy for identifying PL, PO and POL relations.

we explore a much wider variety of syntactic features in this work.


We also notice that 8.3% of the soundbites do not have the correct name hypothesis due to an NER boundary error, and that 12.5% is because of missing errors.

using all the features before the soundbites achieves comparable performance to using all the features, indicating that the region before a soundbite contributes more than that after it.


(2006) perform this computation by: where While the second term of the covariance is easy to compute, the first term requires calculation of quadratic feature expectations.

unlike the previous method, here the first term can be efficiently calculated as well.


Summarizing these results, RH is much faster than Collins model 3 and the reduced version of XLE, but a bit slower than Sagae-Lavie.

table 2 primarily compares the accuracy of the Collins model 3 and RH parsers.


Furthermore, a relationship between an entity pair might be expressed with the implication of the principal entity in some cases.

among 39,467  entities collected from all principal and secondary entities, we randomly select 3,300 entities and manually annotate their types for the Entity Classifier.


TV WEB TRANSCRIPTS Most of our acoustic and language model training data comes from broadcast news.

some challenges for the cross-adaptations had to be overcomed, for instance to cross adapt the non-vowelized system on the vowelized system, we had to remove the vowels to have a nonvowelized transcript.


The article  explored an automatic method to learn an SFST from a bilingual set of samples for machine translation purposes, the so-called GIATI (Grammar Inference and Alignments for Transducers Inference).

each multilingual sample is transformed into a single string from an extended vocabulary (Γ ⊆ using a labelling function (L m ).


Each source word is then joined with a target phrase of each language as the corresponding segmentation suggests.

both languages differs greatly in syntax and in semantics.


The most remarkable difference when comparing both systems is that the N -gram based system produces a relatively large amount of extra words (approximately 10%), while for the phrasebased system, this is only a minor problem (2% of the errors).

the N -gram based system produced more accurate translations, but also a larger amount of extra (incorrect) words when compare to the phrase-based translation system.


Using finer affixes reduces the n-gram language model span, and leads to poor performance for a fixed n-gram size.

the first entry (37.59) is the oracle BLEU score for the N-best list.


For example, in Arabic and to a certain extent in French, some words can be masculine/feminine or singular/plural.

the information we used from MLPT in Fig.


With an N-best list of 30, the system has a very low failure rate for all conditions.

we decided to distinguish the first stressed and the first unstressed syllable from all other stressed and unstressed syllables in the word, in order to encode separate statistics for the privileged first position.


The selected paragraphs consist of 36 sentences.

the error ratio, defined as the ratio of the number of errors to the number of words in output sentence, ranges from 4.76% to 61.54%.


We employ this method as a baseline system, in which NEs are identified by the auto-matic NE recognizers and dictionary lookups as introduced in §2.

it shows that the inclusion of curated data in the semi-  Table 2: Name-only and name-path evaluation results.


As the TIMIT corpus provides phone level segmentations, P t is observed during training.

as shown, t 1 and t 4 are the start and end times respectively for phone p 1 , while t 4 and t 7 are the start and end times for phone p 2 .


A similar case occurs at the start of phone p 1 and the end of phone p 2 .

to the task of determining the phone boundary, identifying one frame per word unit is much simpler, less prone to error or disagreement, and less costly (Greenberg, 1995).


The independent variable in our study is the method of text entry used: (1) letter-by-letter typing using the Wivik keyboard with no word prediction, (2) letter-by-letter typing augmented with word predictions produced by a basic prediction method, (3) letter-by-letter typing augmented with word predictions produced by an advanced prediction method.

while overall communication rate was significantly faster with advanced prediction, users took 0.641 seconds longer for each key press from using advanced prediction compared to entry without prediction.


To choose between null, the, or a/an, the language model in effect constructs Equations 6, 7 and 8 and we pick the one that has the highest probability.


(3) Highway officials insist the ornamental railings on older bridges aren't strong enough to prevent vehicles from crashing through.

the probability of a parse is given by the equation where l(c) is the label of c (e.g., whether it is a noun phrase NP, verb phrase, etc.)


We propose a variation of the SO-PMI algorithm for Japanese, for use in Weblog Opinion Mining.

customer reviews of products, forums, discussion groups, and blogs.


The closer they are, the more likely that they should be combined.

in this paper we propose a cascaded hybrid model for Chinese NER.


Recent approaches to Chinese NER are a shift away from manually constructed rules or finite state patterns towards machine learning or statistical methods.

• The BIO 3 chunk tags of the previous 3 characters.


Results (see Table 6) showed that all the modules got reasonable accuracy except for the sentence root finder.

improving the two root finders is an important task in our future work.


In Section 4 we describe our summarizer and these features used in experiments.

our summarizer contains the preprocessing stage and the estimating stage.


In the same Mandarin broadcast program, the distribution and flow of summary sentences are relatively consistent.

researchers commonly use acoustic/prosodic variation -changes in pitch, intensity, speaking rate -and du-ration of pause for tagging the important contents of their speeches (Hirschberg, 2002).


Most commonly, we use sentences to model individual pieces of information.

for entity nuggets, we examine subtrees headed by "NP"; for event nuggets, subtrees headed by "VP" are examined and their corresponding subjects (siblings headed by "NP") are treated as entity attachments for the verb phrases.


These features are Lesk-style features (Lesk, 1986) that exploit overlaps between glosses of target and seed senses.

to compare our results to theirs, we apply our full model (in 10-fold cross validation experiments) to their data sets.


A feature is created for each vector entry whose value is the count at that position.

for comparison, the first row repeats the results for the mixed corpus from Table 1.


=> drawback -(the quality of being a hindrance; "he pointed out all the drawbacks to my plan") The following objective examples are given in WM: The alarm went off.

f-measure for the Overlaps ablation is not significantly different (p = .39).


Through manual review and empirical testing on data, (Wiebe and Riloff, 2005) divided the clues into strong (strongsubj) and weak (weaksubj) subjectivity clues.

we opted to treat them as one ablation group (Gloss vector).


These methods acquire contextual information directly from unannotated raw text, and senses can be induced from text using some similarity measure (Lin, 1997).

for example, Lesk disambiguated two words by finding the pair of senses with the greatest word overlap in their dictionary definitions (Lesk, 1986).


It is estimated that every year around 2,500 new words appear in English (Kister, 1992).

in our method parsing is not performed in real time when we disambiguate words.


Clinicians make use of additional information beyond children's speech, such as parent and teacher questionnaires and test scores on different language assessment tasks.

a combined lexicon approach was used to tag the mixedlanguage fragments.


Tag Parameters: Draw an infinite sequence of sets i is a distribution over the tagset T ℓ .

for example, the closed form for integrating over the parameter of a superlingual tag with value z is given by: where count(z, y i , ℓ) is the number of times that tag y i is observed together with superlingual tag z in language ℓ, count(z, ℓ) is the total number of times that superlingual tag z appears with an edge into language ℓ, and ω 0 is a hyperparameter.


However, the bilingual model explicitly joins each aligned word-pair into a single coupled state.

when a complete tag lexicon is provided, our unsupervised model achieves an average accuracy of 95%, in comparison to 91% for an unsupervised monolingual Bayesian HMM and 97.4% for its supervised counterpart.


We explicitly sample only part-of-speech tags y, superlingual tags z, and the hyperparameters of the transition and emission Dirichlet priors.

our multilingual model posits latent cross-lingual tags without explicitly joining or directly connecting the part-of-speech tags across languages.


, {α x } Label(α x @a) = ε Foot Axiom α x @a • , i, j, k, l, Λ α x @a • , i, j, k, l, Λ Figure 6: Axioms and inference rules for the CKY algorithm for delayed TL-MCTAG with a delay of d. lay list, giving a total number of active delay lists of O(|G| t(1+d(f −1)) ).

we note that in order to allow a non-total ordering of the trees in a vector we would simply have to record all trees in a tree vector in the histories as is done in the delayed TL-MCTAG parser.


To extract dependency relations from these parse trees, we scan for attachment rules (e.g., L 1 H → Y A L ′ H ) and record that A depends on H. The schema omits the rules for right arguments since they are symmetric.

the posterior P (θ|s, t, α) is also a product of Dirichlets, also factoring into a Dirichlet for each nonterminalN , where the parameters αr are augmented by the number of times rulē r is observed in tree t: We can see that αr acts as a pseudocount of the number of timesr is observed prior to t. To make use of this prior, we use the Variational Bayes (VB) technique for PCFGs with Dirichlet Priors presented by Kurihara and Sato (2004).


This is illustrated by the following example (from the IWSLT 2007 Arabic-English translation task): Source 1: Asf lA ymknk *lk hnAk klfp HwAly vmAnyn dwlAr lAlsAEp AlwAHdp Ref: sorry you can't there is a cost the charge is eighty dollars per hour 1-best: i'm sorry you can't there in the cost about eighty dollars for a one o'clock Source 2: E*rA lA ymknk t$gyl AltlfAz HtY tqlE AlTA}rp Ref: sorry you cannot turn the tv on until the plane has taken off 1-best: excuse me i you turn tv until the plane departs The phrase lA ymknk (you may not/you cannot) is translated differently (and wrongly in the second case) due to different segmentations and phrase translations chosen by the decoder.

the parameter values were 0.5 for the gap penalty, a maximum substring length of k = 4, and weights of 0, 0.1, 0.2, 0.7.


Due to hardware limitation, we are not able to fit the unfiltered phrase tables completely into the memory.

many words remain unaligned on account of their very low frequency.


We choose to focus on the items on these lists that seem most likely to be effective cues for our task.

to put it another way: apparently plausible candidates that often appear in sentences with multiple good candidates (i.e., piggybackers) receive a low distilled score, despite a high initial score.


We mentioned in the introduction some significant challenges to developing a machine-learning approach to discovering DE operators.

very little of this work is computational.


Like Collins and Singer (1999), we assume that the named entities have already been correctly extracted from the text, and our task is merely to label them.

even without the pronoun features, that is, using the same feature set, our system scores equivalently to the EM model, at 83% (this score is   on dev, 25% people).


In the machine translation literature, this process is commonly referred to as decoding.

each node represents a point in time and arcs between nodes indicates a word occurs between the connected nodes' times.


We note, however, that since these are not true phonemes (but rather phonemes copied over from pronunciation dictionaries and word transcripts), we must cautiously interpret these results.

(1997) had previously used phoneme lattices, although with ad hoc edit costs and without efficient indexing.


For example, if we have never seen the word houses in language model training, but have examples of house, we still can expect houses are to be more probable than houses fly.

a lattice is a directed acyclic graph that is used to compactly represent the search space for a speech recognition system.


We accomplish this goal by ranking the set of utterances by our confidence that they contain the query word, a task known as Ranked Utterance Retrieval (RUR).

we could take the most probable degradations until their cumulative probability exceeds some threshold γ.


F (i, j) is the harmonic mean of recall (fraction of the messages in the i also present in j) and precision (fraction of messages in j also present in i), and F is a weighted sum over all ground-truth conversations (i.e., F is microaveraged).

another thread of related work is document expansion.


Segmented Corpus hnAk w-vlAv-wn bn-w Al-ywm Al-jmAEp Morpheme Feature:Value hnAk:1 w:2 vlAv:1 wn:1 bn:1 Al:2 ywm:1 jmAEp:1 hnAk:1 wvlAvwn:1 bnw:1 Alywm:1 Alj-mAEp:1 Bigram Context Feature:Value ## vl:1 #w wn:1 Av ##:1 ## w#:1 bn ##:1 ## yw:1 Al ##:2 ## jm:1 ## ##:5 Furthermore, the corresponding features for the segmented word w-vlAv-wn are shown in Figure 1.

we observe that the maximum number of morphemes that a word contains is usually a small constant for many languages; in the Arabic Penn Treebank, the longest word contains 14 characters, but the maximum number of morphemes in a word is only 5.


Finally, we note that several of the features (the third-and eighth-ranked reward and twelfthranked penalty) shape the translation of shuo 'said', preferring translations with an overt complementizer that and without a comma.

this seems to be because the is often part of a fixed phrase, such as the White House, and therefore comes naturally as part of larger phrasal rules.


Coarse-to-fine parse + rerank employed both of these approximations.

each n-ary rule consists of a root symbol, a sequence of lexical items and non-terminals on the source-side, and a fragment of a syntax tree on the target side.


The Syntax-Augmented MT model of Zollmann and Venugopal (2006), for instance, produces a very large nonterminal set using "slash" (NP/NN → the great) and "plus" labels (NP+VB → she went) to assign syntactically motivated labels for rules whose target words do not correspond to constituents in phrase structure parse trees.

the true score of each translation is "fragmented" across many derivations, so that each translation's most probable derivation is the only one that matters.


Our implementation of the deterministic dependency parser using maximum entropy models as the underlying classifiers achieves 87.8% labeled attachment score and 88.8% unlabeled attachment score on standard Penn Treebank evaluation.

in the rest of the paper, we first introduce our dependency parser based reordering approach based on the analysis of the key issues when translating SVO languages to SOV languages.


The features are classified with special considerations of phrase lengths.

we enumerate all reordering examples, rather than only extract the smallest straight and largest inverted examples.


We now present our method to automatically discover high quality wish templates using the WISH corpus.

a template receives a high score if it is "used" by many frequent wishes but does not match many frequent content-only wishes.


8 Let freq(x j ; d) denote the number of occurrences of the jth word in the vocabulary in document d. where N is the number of documents in the training set.

das and Chen (2001) and Antweiler and Frank (2004) ask whether messages posted on message boards can help explain stock performance, while Li (2005) measures the association between frequency of words associated with risk and subsequent stock returns.


We consider a text regression problem: given a piece of text, predict a R-valued quantity associated with that text.

the word otc refers to "over-the-counter" trading, a high-risk market.


It might initially seem that transliteration is an easy task, requiring only finding a phonetic mapping between character sets.

for Russian, we compare to the model presented in (Klementiev and Roth, 2006b), a weakly supervised algorithm that uses both phonetic information and temporal information.


report 88.53% word accuracy for their SbA technique using leave-one-out testing on the entire NETtalk set (20K words).

the SVM sometimes overgeneralizes, as in the last example in Table 4.


As Table 1 shows, across all grammars and conditions after 2,000 iterations incremental initialization produces samples with much better word segmentation token f-score than does batch initialization, with the largest improvement on the unigram adaptor grammar.

pitman-Yor processes can control the strength of this effect somewhat by moving mass from existing tables to the base distribution.


We identified and corrected the following sources of inconsistencies: Periods and abbreviations.

this paper is based on work funded in part by the Defense Advanced Research Projects Agency through IBM.


2 mn( n 2 m ) m ), but the actual runtime is reduced by several orders of magnitude.


But the CCG account is a competence model as well as a performance model, in that it seeks to unify category representations used in processing with learned generalizations about argument structure; whereas the model described in this paper is exclusively a performance model, allowing generalizations about lexical argument structures to be learned in some other representation, then combined with probabilistic information about parsing strategies to yield a set of derived incomplete constituents.

the remainder of this paper is organized as follows: Section 2 describes related approaches to parsing with stack bounds; Section 3 describes an existing bounded-stack parsing framework using a rightcorner transform defined over individual trees; Section 4 describes a redefinition of this transform to ap-ply to entire probabilistic grammars, cast as infinite sets of generable trees; and Section 5 describes an evaluation of this transform on the Wall Street Journal corpus of the Penn treebank showing improved results for a transformed bounded-stack version of a probabilistic grammar over the original unbounded grammar.


The language models can be integrated out analytically (Section 3.1).

iterative updates of this form are applied until the change in the lower bound is less than 10 −3 .


The fraction represents the posterior estimate of the language models: standard Dirichlet-multinomial conjugacy gives a sum of counts plus a Dirichlet prior (equation 3).

we note two orthogonal but related approaches to extracting nonlinear discourse structures from text.


Previous sections have treated the content of a document set as a single (perhaps learned) unigram distribution.

in order to ensure tight lexical cohesion amongst the specific topics, we assume that each sentence draws a single specific topic Z S used for every specific content word in that sentence.


Finally, in section 5 we sum up and point to future directions.

we use a trigram language model that is based on the puzzle domain transcriptions.


To determine the search radius, we need a working definition of "local listing".

we wanted to test using queries for which we know there is a matching listing in the city/state provided by the caller.


However, every entry in the training pronunciation dictionary is a fully diacritized word mapped to a set of possible contextdependent pronunciations.

we use this system as our baseline for comparison.


Table 2 presents the comparison of BASEWR with the XWR system.

the speech signal is mapped to a more accurate representation of the training transcript, which we hypothesize will lead to a better estimation of the acoustic models.


This effectively builds a skeleton of the desired lattice and delays the creation of the final word lattice until a single replacement operation is carried out in the top cell (S, 1, J).

each still represents the entire search space of all translation hypotheses covering the span.


This suggests that our construct-driven approach yields pronunciation features that are empirically comparable or even better than the amscore.

endeavors into automated scoring for spontaneous speech have been sparse given the challenge of both recognizing and assessing spontaneous speech.


The amount of data for language modeling is orders of magnitude less than that of the acoustic data in continuous space.

we also realize that different problems such as segmentation (e.g.


Then, given a hypothesized sequence of words the decoder extracts the corresponding feature vectors.

we were able to estimate the GMLM probabilities only for words that have at least 50 or more examples.


One obvious choice is to use Maximum Likelihood (ML) criterion.

rapid adaptation for acoustic modeling, using such methods as Maximum Likelihood Linear Regression (MLLR) (Legetter & Woodland, 1995), is possible using very small amount of acoustic data, thanks to the inherent structure of acoustic models that allow large degrees of parameter tying across different words (several thousand context dependent states are shared by all the words in the dictionary).


We apply this idea to motivate two language models: a novel class-based language model and regularized minimum discrimination information (MDI) models.

one can improve exponential language models by adding features (or a prior model) that shrink parameter values while maintaining training performance, and from this observation we develop Heuristics 1 and 2.


We train our model using empirical Bayesian estimation.

users are ordered by their posterior probabilities.


We take F and O as two random variables, and the question of constructing or clustering the object groups can be defined as finding compressed representation of each variable that reserves the information about another variable as high as possible.

we consider this is due partly to the sparseness of the cor-pus, by enlarging the scale of the corpus or using the search engine (e.g.


More and more people are willing to record their feelings (blog), give voice to public affairs (news review), express their likes and dislikes on products (product review), and so on.

to existing explicit adjacency approaches, the proposed approach detects the sentiment association between F and O based on review feature categories and opinion word groups gained from the review corpus.


Typically we use relative-frequency estimation of Pr [c] and Pr[f i | c] for c ∈ {C + , C − }.

learning from noisy examples has been studied for a long time in the learning theory community (Angluin and laird, 1988).


There are several methods to extract useful information from very large corpora.

most predicate argument relations concerning nouns with copula were easily recognized from syntactic preference, and thus the low coverage would not quite affect the performance of discourse analysis.


This far surpasses the ML-PCFG (F1 of 70.7%), and is similar to Zuidema's (2007) DOP result of 83.8%.

the grammar sizes are not strictly comparable, as the Berkeley binarised grammars prohibit non-binary rules, and are therefore forced to decompose each of these rules into many child rules.


These rules mostly describe cases when the S category is used for a full sentence, which most often include punctuation such as the full stop and quotation marks.

tree substitution grammars (tSGs) are a compelling alternative to context-free grammars for modelling syntax.


This grammar differs from the state split grammars in that it factors into two separate projections, a dependency projection and a PCFG.

ing coarser projections falls off for HA * , the work saved with CTF increases with the addition of highly coarse grammars.


In standard CTF, we exhaustively parse in each projection level, but skip edges whose projections in the previous level had sufficiently low scores.

since the weights of the rules in the smaller grammars are the minimum of a large set of rules in the target grammar, these grammars have costs that are so cheap that all edges in those grammars will be processed long before much progress is made in the refined, more expensive levels.


These predicates can be used recursively at every level of the tree to specify the relation between the most important segments.


(2008)], and distributional methods [e.g., Bergsma et al.

at the same time, we want to avoid assigning similar objects to different classes.


Second, we compare our cut-based approach with the five aforementioned approaches to anaphoricity determination (namely, Ng and Cardie (2002a), Ng (2004), Luo (2007), Denis and Baldridge (2007), and Finkel and Manning (2008)) in terms of their effectiveness in improving a learning-based coreference system.

luo's algorithm attempts to find the most probable coreference partition of a given set of mentions.


To some extent this may be due to the fact that Trimmer uses smaller (trimmed) fragments of source sentences in its summaries.

one or two summarizers still tended to do well.


The Eisner algorithm, originally designed for generative parsing, decomposes the probability of a dependency parse into the probabilities of each attachment of a dependent to its parent, and the probabilities of each parent stopping taking dependents.

the model parameters, θ , are normally distributed, with mean µ (typically zero) and variance σ 2 .


Arguably MUC-6 and MUC-7 should not count as separate domains, but because they were annotated separately, for different shared tasks, we chose to treat them as such, and feel that our experimental results justify the distinction.

when a domain lacks evidence for a parameter the opposite occurs, and the prior (whose value is determined by evidence in the other domains) will have a greater effect on the parameter value.


For large datasets, these s i s might not even fit in memory, and resorting to physical disk would be very slow.

intuitively, updates become more reliable with larger m, so we can afford to trust them more and incorporate them more aggressively.


We get a similarly striking result for document classification, but the results for word segmentation and word alignment are more modest.

sEM can be seen as stochastic gradient in the space of sufficient statistics.


In order to leverage the sentence information, we adjust a word's weight by the salience scores of the sentences containing that word.

we also find that this assumption also holds using statistics obtained from the meeting corpus used in this study.


(Matsuo and Ishizuka, 2004) proposed a co-occurrence distribution based method using a clustering strategy for extracting keywords for a single document without relying on a large corpus, and reported promising results.

the graph method does not perform as well as the TFIDF approach.


when the system claims the floor, the state can be SY ST EM , BOT H S , or BOT H U ).